Natural Language Processing Using Playlist Access Matrix
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Solution Overview
Problem
Conventional natural language processing systems require large language ontology databases to achieve accurate command matching, which is not feasible for local devices due to storage constraints and the need for network access.
Innovation Solution
A natural language processing system that uses a playlist access matrix to identify and match command elements in natural language commands, allowing for local execution of computing language commands without relying on extensive cloud-based databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language processing systems use large language ontology databases to achieve accurate command matching, then measurement precision is improved, but device complexity and storage requirements worsen
Solution Approach 1:
The patent segments the natural language command into individual words/tokens and processes each token separately through the playlist access matrix. Instead of matching the entire command string against a large ontology database, the system divides the command into component words (e.g., 'turn', 'on', 'lights') and uses each as a separate key to access the matrix, thereby reducing the complexity and size of the database while maintaining matching accuracy.
Solution Approach 2:
The patent extracts only the essential command elements (words/tokens) from the natural language command that are relevant for matching. By identifying and extracting key tokens like action verbs and objects, the system avoids storing and processing redundant information in the database, thus reducing database size while preserving the core functionality for accurate command interpretation.
2Measurement precision
If sophisticated natural language processing is provided via cloud-based systems with growing language ontology databases, then measurement precision is improved, but loss of time for network access and processing worsens
Solution Approach 1:
The patent performs preliminary action by pre-structuring the playlist access matrix in a compact format that can be stored locally on the device. The matrix is organized with words/tokens as keys and playlist pointers as values, allowing immediate local lookup without requiring network access. This preliminary preparation enables the system to process natural language commands locally and instantly, eliminating network latency while maintaining processing accuracy.
3Measurement precision
If artificial intelligence is used to allow the natural language processing system to learn new commands and provide more accurate matches, then measurement precision is improved, but device complexity and storage requirements worsen
Solution Approach 1:
The patent uses a simplified copying mechanism where the playlist access matrix stores pre-computed playlist pointers that reference command playlists. Instead of implementing complex AI learning algorithms that require large databases, the system copies and stores only the essential mapping information (word/token to playlist pointer) in a compact matrix format. This allows the system to maintain accurate command matching while keeping the database size minimal and suitable for local devices.
Data Source
AI summary
A natural language processing system identifies an action element, a target element, and command element(s) in a text natural language command. For each identified command element, in the order it appears in the text natural language command, the natural language processing system accesses a playlist access matrix according to a matrix access counter to identify a playlist pointer associated with that command element, determines whether that playlist pointer indicates its associated command element is a best match relative to any other command elements that have already been considered and, if so, updates a playlist entry identifier with that playlist pointer and increments the matrix access counter. When all of the command elements have been considered, the natural language processing system uses the playlist entry identifier to identify a computing language command in a command playlist, and executes the computing language command on the target element based on the action element.